It is known that a significant percentage of falls, especially among senior adults, cause serious injuries, such as broken bones or head injuries. Currently, there are many companies offering fall detection services. However, most, if not all, commercial systems require the user to wear a device, which can be inconvenient. Many attempts have been made to detect falls with systems that do not include wearable devices. These systems have included or relied on depth cameras, sound detectors, radar and radio frequency (RF) signals, floor vibration, etc. Each of these prototypes, however, suffers from one or more limitations, including, but not limited to, low accuracy in certain scenarios, high cost, and/or a lack of security (i.e., privacy concerns).
A system was tested in the homes of senior adults, which used a combination of Doppler radar, a Microsoft KINECT® sensor, and a webcam for fall detection. The results prompted further study of the use of the Microsoft KINECT® sensor, but the system (i) was susceptible to sudden light changes, and, therefore, had difficulty detecting falls occurring at such moments, and (ii) was costlier and/or less acceptable to users with security (e.g., privacy) concerns.
There remains a need for devices and/or systems that can detect falls accurately, are affordable, do not raise substantial security (e.g., privacy) concerns, do not rely on training data that may be difficult to obtain, including high-quality training data, and/or do not include a wearable component.